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Abstract

We describe a novel approach for inducing unsupervised part-of-speech taggers for
languages that have no labeled training data, but have translated text in a
resource-rich language. Our method does not assume any knowledge about the target
language (in particular no tagging dictionary is assumed), making it applicable for
a wide array of resource-poor languages. We use graph-based label propagation for
cross-lingual knowledge transfer and use the projected labels as constraints in an
unsupervised model. Across six European languages, our approach results in an
average absolute improvement of 9.7\% over the state-of-the-art baseline, and
17.0\% over vanilla hidden Markov models induced with EM.